<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The rapid development of computer vision has greatly promoted the development of camera in the fields of target detection, remote sensing analysis, target recognition and so on. As the carrier of information exchange in contemporary society, image contains a large number of information elements. When taking an image, it will blur the image and destroy many photos due to various factors, resulting in the inability to obtain relevant information from the image. Restoring a clear image from the blurred image is a hot spot in the field of computer vision and image processing. This paper expounds the causes of fuzziness in detail, summarizes and combs the current blind deblurring methods and research status based on deep learning, makes a detailed overview of the development of deblurring network based on deep learning from three aspects: convolution neural network, cyclic neural network and generation countermeasure network, and summarizes the advantages and disadvantages of various methods, Some networks are compared, and the problems of feature extraction, evaluation index and data set construction in deblurring are analyzed. Finally, the development trend of image motion blur restoration technology is prospected.</p>
</abstract>

The rapid development of computer vision has greatly promoted the development of camera in the fields of target detection, remote sensing analysis, target recognition and so on. As the carrier of information exchange in contemporary society, image contains a large number of information elements. When taking an image, it will blur the image and destroy many photos due to various factors, resulting in the inability to obtain relevant information from the image. Restoring a clear image from the blurred image is a hot spot in the field of computer vision and image processing. This paper expounds the causes of fuzziness in detail, summarizes and combs the current blind deblurring methods and research status based on deep learning, makes a detailed overview of the development of deblurring network based on deep learning from three aspects: convolution neural network, cyclic neural network and generation countermeasure network, and summarizes the advantages and disadvantages of various methods, Some networks are compared, and the problems of feature extraction, evaluation index and data set construction in deblurring are analyzed. Finally, the development trend of image motion blur restoration technology is prospected.

Development of Blind Deblurring Based on Deep Learning

School of Computer Science and Engineering

International Journal of Advanced Network, Monitoring and Controls

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Development of Blind Deblurring Based on Deep Learning"}

The rapid development of computer vision has greatly promoted the development of camera in the fields of target detection, remote sensing analysis, target...

Development of Blind Deblurring Based on Deep Learning

Data publikacji: 21 maj 2023

Zakres stron: 106 - 114

DOI: https://doi.org/10.21307/ijanmc-2021-040

Słowa kluczowe
Deep Learning, Neural Networks, Computer Vision, Deep Learning, Blind Deblurring Neural Network

© 2021 Shi Kecun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Development of Blind Deblurring Based on Deep Learning

Data publikacji: 21 maj 2023

Zakres stron: 106 - 114

DOI: https://doi.org/10.21307/ijanmc-2021-040

Słowa kluczoweDeep Learning, Neural Networks, Computer Vision, Deep Learning, Blind Deblurring Neural Network

© 2021 Shi Kecun et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Słowa kluczowe
Deep Learning, Neural Networks, Computer Vision, Deep Learning, Blind Deblurring Neural Network